Overview

Dataset statistics

Number of variables14
Number of observations4076
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory307.3 B

Variable types

Numeric9
Categorical4
Boolean1

Warnings

champion has a high cardinality: 153 distinct values High cardinality
accountId has a high cardinality: 420 distinct values High cardinality
gameId is highly correlated with timestampHigh correlation
timestamp is highly correlated with gameIdHigh correlation
accountId is uniformly distributed Uniform
kills has 269 (6.6%) zeros Zeros
deaths has 169 (4.1%) zeros Zeros
assists has 154 (3.8%) zeros Zeros
visionScore has 206 (5.1%) zeros Zeros

Reproduction

Analysis started2021-03-31 23:51:12.597949
Analysis finished2021-03-31 23:51:27.881937
Duration15.28 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

gameId
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3842
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2194442562
Minimum2066803236
Maximum2211510048
Zeros0
Zeros (%)0.0%
Memory size32.0 KiB
2021-03-31T20:51:28.054036image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2066803236
5-th percentile2164662838
Q12188971171
median2198322540
Q32204652064
95-th percentile2209895772
Maximum2211510048
Range144706812
Interquartile range (IQR)15680892.75

Descriptive statistics

Standard deviation15798207.44
Coefficient of variation (CV)0.007199189311
Kurtosis12.87377017
Mean2194442562
Median Absolute Deviation (MAD)7563965
Skewness-2.684345557
Sum8.944547884 × 1012
Variance2.495833583 × 1014
MonotocityNot monotonic
2021-03-31T20:51:28.236380image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22093440123
 
0.1%
22088097193
 
0.1%
22030366693
 
0.1%
22062055403
 
0.1%
21975088483
 
0.1%
22040902983
 
0.1%
21994232033
 
0.1%
21957899053
 
0.1%
22080643123
 
0.1%
22107563263
 
0.1%
Other values (3832)4046
99.3%
ValueCountFrequency (%)
20668032361
< 0.1%
20668058761
< 0.1%
20668207231
< 0.1%
20668997481
< 0.1%
20669208261
< 0.1%
20669251051
< 0.1%
20669553061
< 0.1%
20670112391
< 0.1%
20670646261
< 0.1%
20670702251
< 0.1%
ValueCountFrequency (%)
22115100481
< 0.1%
22114680271
< 0.1%
22114677271
< 0.1%
22114367121
< 0.1%
22114314601
< 0.1%
22114306841
< 0.1%
22114260391
< 0.1%
22114176901
< 0.1%
22114126251
< 0.1%
22114116891
< 0.1%

champion
Categorical

HIGH CARDINALITY

Distinct153
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
145
 
124
150
 
100
412
 
92
236
 
72
18
 
72
Other values (148)
3616 

Length

Max length3
Median length3
Mean length2.487242395
Min length1

Characters and Unicode

Total characters10138
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row40
2nd row40
3rd row117
4th row40
5th row40
ValueCountFrequency (%)
145124
 
3.0%
150100
 
2.5%
41292
 
2.3%
23672
 
1.8%
1872
 
1.8%
7968
 
1.7%
20268
 
1.7%
16467
 
1.6%
8166
 
1.6%
6766
 
1.6%
Other values (143)3281
80.5%
2021-03-31T20:51:28.637686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
145124
 
3.0%
150100
 
2.5%
41292
 
2.3%
1872
 
1.8%
23672
 
1.8%
7968
 
1.7%
20268
 
1.7%
16467
 
1.6%
8166
 
1.6%
6766
 
1.6%
Other values (143)3281
80.5%

Most occurring characters

ValueCountFrequency (%)
12102
20.7%
21536
15.2%
41258
12.4%
51001
9.9%
3975
9.6%
6838
 
8.3%
7780
 
7.7%
0640
 
6.3%
8561
 
5.5%
9447
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10138
100.0%

Most frequent character per category

ValueCountFrequency (%)
12102
20.7%
21536
15.2%
41258
12.4%
51001
9.9%
3975
9.6%
6838
 
8.3%
7780
 
7.7%
0640
 
6.3%
8561
 
5.5%
9447
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common10138
100.0%

Most frequent character per script

ValueCountFrequency (%)
12102
20.7%
21536
15.2%
41258
12.4%
51001
9.9%
3975
9.6%
6838
 
8.3%
7780
 
7.7%
0640
 
6.3%
8561
 
5.5%
9447
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10138
100.0%

Most frequent character per block

ValueCountFrequency (%)
12102
20.7%
21536
15.2%
41258
12.4%
51001
9.9%
3975
9.6%
6838
 
8.3%
7780
 
7.7%
0640
 
6.3%
8561
 
5.5%
9447
 
4.4%

timestamp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3842
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.613977192 × 1012
Minimum1.600711815 × 1012
Maximum1.615679811 × 1012
Zeros0
Zeros (%)0.0%
Memory size32.0 KiB
2021-03-31T20:51:28.806197image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1.600711815 × 1012
5-th percentile1.611076564 × 1012
Q11.613425192 × 1012
median1.61436582 × 1012
Q31.615002967 × 1012
95-th percentile1.615521737 × 1012
Maximum1.615679811 × 1012
Range1.496799599 × 1010
Interquartile range (IQR)1577774586

Descriptive statistics

Standard deviation1602268040
Coefficient of variation (CV)0.000992745156
Kurtosis14.37666774
Mean1.613977192 × 1012
Median Absolute Deviation (MAD)767664442
Skewness-2.828026483
Sum6.578571036 × 1015
Variance2.567262871 × 1018
MonotocityNot monotonic
2021-03-31T20:51:28.999231image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.614960453 × 10123
 
0.1%
1.614904334 × 10123
 
0.1%
1.614467134 × 10123
 
0.1%
1.615624316 × 10123
 
0.1%
1.614692744 × 10123
 
0.1%
1.614651624 × 10123
 
0.1%
1.614102879 × 10123
 
0.1%
1.614314672 × 10123
 
0.1%
1.614865428 × 10123
 
0.1%
1.615339278 × 10123
 
0.1%
Other values (3832)4046
99.3%
ValueCountFrequency (%)
1.600711815 × 10121
< 0.1%
1.600713368 × 10121
< 0.1%
1.600714968 × 10121
< 0.1%
1.600718139 × 10121
< 0.1%
1.600720166 × 10121
< 0.1%
1.600722305 × 10121
< 0.1%
1.600723126 × 10121
< 0.1%
1.600724824 × 10121
< 0.1%
1.600727004 × 10121
< 0.1%
1.600728715 × 10121
< 0.1%
ValueCountFrequency (%)
1.615679811 × 10121
< 0.1%
1.615679251 × 10121
< 0.1%
1.615679083 × 10121
< 0.1%
1.61567842 × 10121
< 0.1%
1.615678224 × 10121
< 0.1%
1.615677655 × 10121
< 0.1%
1.615675696 × 10121
< 0.1%
1.615675687 × 10121
< 0.1%
1.615675405 × 10121
< 0.1%
1.615675155 × 10121
< 0.1%

role
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size253.7 KiB
DUO_SUPPORT
1257 
SOLO
1133 
NONE
687 
DUO_CARRY
541 
DUO
458 

Length

Max length11
Median length4
Mean length6.710009814
Min length3

Characters and Unicode

Total characters27350
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDUO_SUPPORT
2nd rowDUO_SUPPORT
3rd rowDUO_SUPPORT
4th rowDUO_SUPPORT
5th rowDUO_SUPPORT
ValueCountFrequency (%)
DUO_SUPPORT1257
30.8%
SOLO1133
27.8%
NONE687
16.9%
DUO_CARRY541
13.3%
DUO458
 
11.2%
2021-03-31T20:51:29.341327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-03-31T20:51:29.698660image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
duo_support1257
30.8%
solo1133
27.8%
none687
16.9%
duo_carry541
13.3%
duo458
 
11.2%

Most occurring characters

ValueCountFrequency (%)
O6466
23.6%
U3513
12.8%
P2514
 
9.2%
S2390
 
8.7%
R2339
 
8.6%
D2256
 
8.2%
_1798
 
6.6%
N1374
 
5.0%
T1257
 
4.6%
L1133
 
4.1%
Other values (4)2310
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter25552
93.4%
Connector Punctuation1798
 
6.6%

Most frequent character per category

ValueCountFrequency (%)
O6466
25.3%
U3513
13.7%
P2514
 
9.8%
S2390
 
9.4%
R2339
 
9.2%
D2256
 
8.8%
N1374
 
5.4%
T1257
 
4.9%
L1133
 
4.4%
E687
 
2.7%
Other values (3)1623
 
6.4%
ValueCountFrequency (%)
_1798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25552
93.4%
Common1798
 
6.6%

Most frequent character per script

ValueCountFrequency (%)
O6466
25.3%
U3513
13.7%
P2514
 
9.8%
S2390
 
9.4%
R2339
 
9.2%
D2256
 
8.8%
N1374
 
5.4%
T1257
 
4.9%
L1133
 
4.4%
E687
 
2.7%
Other values (3)1623
 
6.4%
ValueCountFrequency (%)
_1798
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII27350
100.0%

Most frequent character per block

ValueCountFrequency (%)
O6466
23.6%
U3513
12.8%
P2514
 
9.2%
S2390
 
8.7%
R2339
 
8.6%
D2256
 
8.2%
_1798
 
6.6%
N1374
 
5.0%
T1257
 
4.6%
L1133
 
4.1%
Other values (4)2310
 
8.4%

lane
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size244.8 KiB
BOTTOM
1032 
NONE
860 
MID
758 
TOP
739 
JUNGLE
687 

Length

Max length6
Median length4
Mean length4.476202159
Min length3

Characters and Unicode

Total characters18245
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOTTOM
2nd rowBOTTOM
3rd rowNONE
4th rowNONE
5th rowNONE
ValueCountFrequency (%)
BOTTOM1032
25.3%
NONE860
21.1%
MID758
18.6%
TOP739
18.1%
JUNGLE687
16.9%
2021-03-31T20:51:29.985586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-03-31T20:51:30.093117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
bottom1032
25.3%
none860
21.1%
mid758
18.6%
top739
18.1%
jungle687
16.9%

Most occurring characters

ValueCountFrequency (%)
O3663
20.1%
T2803
15.4%
N2407
13.2%
M1790
9.8%
E1547
8.5%
B1032
 
5.7%
I758
 
4.2%
D758
 
4.2%
P739
 
4.1%
J687
 
3.8%
Other values (3)2061
11.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter18245
100.0%

Most frequent character per category

ValueCountFrequency (%)
O3663
20.1%
T2803
15.4%
N2407
13.2%
M1790
9.8%
E1547
8.5%
B1032
 
5.7%
I758
 
4.2%
D758
 
4.2%
P739
 
4.1%
J687
 
3.8%
Other values (3)2061
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin18245
100.0%

Most frequent character per script

ValueCountFrequency (%)
O3663
20.1%
T2803
15.4%
N2407
13.2%
M1790
9.8%
E1547
8.5%
B1032
 
5.7%
I758
 
4.2%
D758
 
4.2%
P739
 
4.1%
J687
 
3.8%
Other values (3)2061
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII18245
100.0%

Most frequent character per block

ValueCountFrequency (%)
O3663
20.1%
T2803
15.4%
N2407
13.2%
M1790
9.8%
E1547
8.5%
B1032
 
5.7%
I758
 
4.2%
D758
 
4.2%
P739
 
4.1%
J687
 
3.8%
Other values (3)2061
11.3%

accountId
Categorical

HIGH CARDINALITY
UNIFORM

Distinct420
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size420.0 KiB
ghO_eGMixhiLZYXoewf6ebQNLrDUbW94LdS48fce1c82Znk
 
10
l8j0YLFxmS5MvyAejTa2hmhEac-QrCWo82UDB7hgYWaq5O0
 
10
TcDPzFeNE-EhzL15Uj57PsdXDgTKrCs1HQc94RjZJbINsaQ
 
10
myUIWS0xKWSz53CzD418yNAdI0I7C82B10dyjag3DAuBol0
 
10
CmbTtWG79t5SsXy5eKufrF5g8R-VzHaeCZA8iZNk7vSflz0
 
10
Other values (415)
4026 

Length

Max length56
Median length47
Mean length48.47620216
Min length43

Characters and Unicode

Total characters197589
Distinct characters64
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs
2nd row2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs
3rd row2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs
4th row2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs
5th row2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs
ValueCountFrequency (%)
ghO_eGMixhiLZYXoewf6ebQNLrDUbW94LdS48fce1c82Znk10
 
0.2%
l8j0YLFxmS5MvyAejTa2hmhEac-QrCWo82UDB7hgYWaq5O010
 
0.2%
TcDPzFeNE-EhzL15Uj57PsdXDgTKrCs1HQc94RjZJbINsaQ10
 
0.2%
myUIWS0xKWSz53CzD418yNAdI0I7C82B10dyjag3DAuBol010
 
0.2%
CmbTtWG79t5SsXy5eKufrF5g8R-VzHaeCZA8iZNk7vSflz010
 
0.2%
sLHs4F9dqfzfzTXb5bMBJCLwexgiW8B6WnfkN-13L1w10
 
0.2%
WDRPFKmpseGpjP6ulI-LHgte5b4rNsBEDQL-D6lb0E3ONhui_hiToUS610
 
0.2%
rH9qlhXt1qunw4HGETlnROTIcfUqxNehLqbJF5596cPbub0H3_05_Jj510
 
0.2%
HQH_LK3WAH1Twx-38qww6WSOR9mFLFyR-_x6TDlCj7Kd4y410
 
0.2%
mXeFjQbqS_bi1XgZy8z6uK_MxW8662ynwrQM0Pu7EBk10
 
0.2%
Other values (410)3976
97.5%
2021-03-31T20:51:30.441813image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
br-u_nyylg5o9hhif69bxgxhr_s_hc8fjjyc_kaclldordyd-qmhzr_o10
 
0.2%
pexq7byqrv24t4lpxmv4usrigfxxnkmueew2qu_4q5hgya010
 
0.2%
aazaxevcz4jwtxebbupwm_4vjymvyh99kswf0l3nywkhwh5rlq1b32rj10
 
0.2%
jvxhhe1xlsy1g095gxbgsqawverlygpdhgomwh4rzstg10
 
0.2%
r0ooblvj-wtcbhrrzgy9m8naejjqkqxpnlcqpci7bfqqnveivlbruvp10
 
0.2%
gee2vnfuovgr3rvzchmpuh1w4xtfre0i37gvgwrp3eovvgc10
 
0.2%
ddhblqjpwb-crwfcu1rcyuat8ov7ugjrth2mnpieuen4keu10
 
0.2%
fkg0ibfjdqffgg8xjnvvuj3ydpbuulqgxcla4zioszkn1kg10
 
0.2%
vxxff4zk14ttymeluoeyqtchppa5q13wprj3txg56abjk9010
 
0.2%
kt3uve9adwijgebrrte3uycxe2rqqizs-skux5-d5jl3neklg77a9omi10
 
0.2%
Other values (410)3976
97.5%

Most occurring characters

ValueCountFrequency (%)
x3451
 
1.7%
k3366
 
1.7%
03366
 
1.7%
Q3335
 
1.7%
83333
 
1.7%
o3305
 
1.7%
A3303
 
1.7%
M3299
 
1.7%
U3294
 
1.7%
Y3287
 
1.7%
Other values (54)164250
83.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter80335
40.7%
Uppercase Letter80154
40.6%
Decimal Number30945
 
15.7%
Connector Punctuation3082
 
1.6%
Dash Punctuation3073
 
1.6%

Most frequent character per category

ValueCountFrequency (%)
x3451
 
4.3%
k3366
 
4.2%
o3305
 
4.1%
d3281
 
4.1%
y3265
 
4.1%
e3233
 
4.0%
j3194
 
4.0%
r3144
 
3.9%
u3140
 
3.9%
n3138
 
3.9%
Other values (16)47818
59.5%
ValueCountFrequency (%)
Q3335
 
4.2%
A3303
 
4.1%
M3299
 
4.1%
U3294
 
4.1%
Y3287
 
4.1%
W3227
 
4.0%
F3207
 
4.0%
X3205
 
4.0%
H3185
 
4.0%
G3183
 
4.0%
Other values (16)47629
59.4%
ValueCountFrequency (%)
03366
10.9%
83333
10.8%
43249
10.5%
53235
10.5%
13179
10.3%
63143
10.2%
22913
9.4%
32901
9.4%
92872
9.3%
72754
8.9%
ValueCountFrequency (%)
-3073
100.0%
ValueCountFrequency (%)
_3082
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin160489
81.2%
Common37100
 
18.8%

Most frequent character per script

ValueCountFrequency (%)
x3451
 
2.2%
k3366
 
2.1%
Q3335
 
2.1%
o3305
 
2.1%
A3303
 
2.1%
M3299
 
2.1%
U3294
 
2.1%
Y3287
 
2.0%
d3281
 
2.0%
y3265
 
2.0%
Other values (42)127303
79.3%
ValueCountFrequency (%)
03366
9.1%
83333
9.0%
43249
8.8%
53235
8.7%
13179
8.6%
63143
8.5%
_3082
8.3%
-3073
8.3%
22913
7.9%
32901
7.8%
Other values (2)5626
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII197589
100.0%

Most frequent character per block

ValueCountFrequency (%)
x3451
 
1.7%
k3366
 
1.7%
03366
 
1.7%
Q3335
 
1.7%
83333
 
1.7%
o3305
 
1.7%
A3303
 
1.7%
M3299
 
1.7%
U3294
 
1.7%
Y3287
 
1.7%
Other values (54)164250
83.1%

participantId
Real number (ℝ≥0)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.648675172
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size32.0 KiB
2021-03-31T20:51:30.569207image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.87902944
Coefficient of variation (CV)0.5096822444
Kurtosis-1.237226077
Mean5.648675172
Median Absolute Deviation (MAD)2
Skewness-0.0732662225
Sum23024
Variance8.288810514
MonotocityNot monotonic
2021-03-31T20:51:30.685095image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8451
11.1%
9435
10.7%
10434
10.6%
6416
10.2%
7415
10.2%
2415
10.2%
4388
9.5%
5376
9.2%
3376
9.2%
1370
9.1%
ValueCountFrequency (%)
1370
9.1%
2415
10.2%
3376
9.2%
4388
9.5%
5376
9.2%
6416
10.2%
7415
10.2%
8451
11.1%
9435
10.7%
10434
10.6%
ValueCountFrequency (%)
10434
10.6%
9435
10.7%
8451
11.1%
7415
10.2%
6416
10.2%
5376
9.2%
4388
9.5%
3376
9.2%
2415
10.2%
1370
9.1%

win
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
True
2228 
False
1848 
ValueCountFrequency (%)
True2228
54.7%
False1848
45.3%
2021-03-31T20:51:30.771649image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

kills
Real number (ℝ≥0)

ZEROS

Distinct36
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.459764475
Minimum0
Maximum50
Zeros269
Zeros (%)6.6%
Memory size32.0 KiB
2021-03-31T20:51:30.878240image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile17
Maximum50
Range50
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.365749169
Coefficient of variation (CV)0.8306416108
Kurtosis3.514087766
Mean6.459764475
Median Absolute Deviation (MAD)3
Skewness1.486234601
Sum26330
Variance28.79126414
MonotocityNot monotonic
2021-03-31T20:51:31.048666image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3405
9.9%
2404
9.9%
1361
 
8.9%
4355
 
8.7%
6314
 
7.7%
5293
 
7.2%
7284
 
7.0%
0269
 
6.6%
8243
 
6.0%
10201
 
4.9%
Other values (26)947
23.2%
ValueCountFrequency (%)
0269
6.6%
1361
8.9%
2404
9.9%
3405
9.9%
4355
8.7%
5293
7.2%
6314
7.7%
7284
7.0%
8243
6.0%
9180
4.4%
ValueCountFrequency (%)
501
 
< 0.1%
371
 
< 0.1%
331
 
< 0.1%
321
 
< 0.1%
314
 
0.1%
305
0.1%
292
 
< 0.1%
2810
0.2%
276
0.1%
267
0.2%

deaths
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.436947988
Minimum0
Maximum20
Zeros169
Zeros (%)4.1%
Memory size32.0 KiB
2021-03-31T20:51:31.203337image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile11
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.251381797
Coefficient of variation (CV)0.5980159833
Kurtosis0.324063309
Mean5.436947988
Median Absolute Deviation (MAD)2
Skewness0.5957088683
Sum22161
Variance10.57148359
MonotocityNot monotonic
2021-03-31T20:51:31.369313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5488
12.0%
4483
11.8%
6471
11.6%
3428
10.5%
7401
9.8%
2374
9.2%
8302
7.4%
1262
6.4%
9247
6.1%
10174
 
4.3%
Other values (11)446
10.9%
ValueCountFrequency (%)
0169
 
4.1%
1262
6.4%
2374
9.2%
3428
10.5%
4483
11.8%
5488
12.0%
6471
11.6%
7401
9.8%
8302
7.4%
9247
6.1%
ValueCountFrequency (%)
201
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
174
 
0.1%
1610
 
0.2%
1527
 
0.7%
1427
 
0.7%
1335
 
0.9%
1264
1.6%
11105
2.6%

assists
Real number (ℝ≥0)

ZEROS

Distinct40
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.201177625
Minimum0
Maximum54
Zeros154
Zeros (%)3.8%
Memory size32.0 KiB
2021-03-31T20:51:31.531785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q311
95-th percentile20
Maximum54
Range54
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.103384043
Coefficient of variation (CV)0.7442082493
Kurtosis2.562538577
Mean8.201177625
Median Absolute Deviation (MAD)4
Skewness1.293108182
Sum33428
Variance37.25129677
MonotocityNot monotonic
2021-03-31T20:51:31.706101image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7330
 
8.1%
4326
 
8.0%
5322
 
7.9%
3304
 
7.5%
2291
 
7.1%
6290
 
7.1%
8253
 
6.2%
9242
 
5.9%
10221
 
5.4%
1199
 
4.9%
Other values (30)1298
31.8%
ValueCountFrequency (%)
0154
3.8%
1199
4.9%
2291
7.1%
3304
7.5%
4326
8.0%
5322
7.9%
6290
7.1%
7330
8.1%
8253
6.2%
9242
5.9%
ValueCountFrequency (%)
541
 
< 0.1%
441
 
< 0.1%
391
 
< 0.1%
382
 
< 0.1%
353
0.1%
341
 
< 0.1%
333
0.1%
326
0.1%
315
0.1%
303
0.1%

visionScore
Real number (ℝ≥0)

ZEROS

Distinct121
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.7563788
Minimum0
Maximum152
Zeros206
Zeros (%)5.1%
Memory size32.0 KiB
2021-03-31T20:51:31.881925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median21
Q332
95-th percentile67.25
Maximum152
Range152
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.83007002
Coefficient of variation (CV)0.7699090844
Kurtosis4.330005989
Mean25.7563788
Median Absolute Deviation (MAD)9
Skewness1.772033687
Sum104983
Variance393.2316771
MonotocityNot monotonic
2021-03-31T20:51:32.067660image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0206
 
5.1%
18155
 
3.8%
20150
 
3.7%
17143
 
3.5%
15137
 
3.4%
13123
 
3.0%
16122
 
3.0%
19121
 
3.0%
12117
 
2.9%
14117
 
2.9%
Other values (111)2685
65.9%
ValueCountFrequency (%)
0206
5.1%
119
 
0.5%
27
 
0.2%
326
 
0.6%
431
 
0.8%
544
 
1.1%
656
 
1.4%
753
 
1.3%
865
 
1.6%
991
2.2%
ValueCountFrequency (%)
1521
 
< 0.1%
1511
 
< 0.1%
1401
 
< 0.1%
1341
 
< 0.1%
1231
 
< 0.1%
1211
 
< 0.1%
1201
 
< 0.1%
1181
 
< 0.1%
1171
 
< 0.1%
1163
0.1%

goldEarned
Real number (ℝ≥0)

Distinct3555
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11018.21246
Minimum671
Maximum34426
Zeros0
Zeros (%)0.0%
Memory size32.0 KiB
2021-03-31T20:51:32.259099image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum671
5-th percentile5001.25
Q18136
median10645
Q313591
95-th percentile18091
Maximum34426
Range33755
Interquartile range (IQR)5455

Descriptive statistics

Standard deviation4190.397563
Coefficient of variation (CV)0.3803155527
Kurtosis1.084987407
Mean11018.21246
Median Absolute Deviation (MAD)2694.5
Skewness0.5566422398
Sum44910234
Variance17559431.73
MonotocityNot monotonic
2021-03-31T20:51:32.443723image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111184
 
0.1%
109834
 
0.1%
114374
 
0.1%
125404
 
0.1%
117684
 
0.1%
128233
 
0.1%
125413
 
0.1%
52003
 
0.1%
89113
 
0.1%
86823
 
0.1%
Other values (3545)4041
99.1%
ValueCountFrequency (%)
6711
< 0.1%
6721
< 0.1%
6741
< 0.1%
6792
< 0.1%
6851
< 0.1%
7071
< 0.1%
7451
< 0.1%
7651
< 0.1%
7771
< 0.1%
7911
< 0.1%
ValueCountFrequency (%)
344261
< 0.1%
320091
< 0.1%
296171
< 0.1%
295531
< 0.1%
288021
< 0.1%
287301
< 0.1%
285611
< 0.1%
277041
< 0.1%
276911
< 0.1%
274991
< 0.1%

totalMinionsKilled
Real number (ℝ≥0)

Distinct310
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.1668302
Minimum0
Maximum389
Zeros14
Zeros (%)0.3%
Memory size32.0 KiB
2021-03-31T20:51:32.635681image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q133
median121
Q3177
95-th percentile234
Maximum389
Range389
Interquartile range (IQR)144

Descriptive statistics

Standard deviation78.39219091
Coefficient of variation (CV)0.6927134988
Kurtosis-1.117899892
Mean113.1668302
Median Absolute Deviation (MAD)75
Skewness0.2058471085
Sum461268
Variance6145.335596
MonotocityNot monotonic
2021-03-31T20:51:32.824639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3148
 
1.2%
2445
 
1.1%
2741
 
1.0%
2340
 
1.0%
3040
 
1.0%
1740
 
1.0%
3239
 
1.0%
1938
 
0.9%
2138
 
0.9%
1138
 
0.9%
Other values (300)3669
90.0%
ValueCountFrequency (%)
014
0.3%
16
 
0.1%
29
 
0.2%
38
 
0.2%
420
0.5%
510
 
0.2%
614
0.3%
725
0.6%
831
0.8%
925
0.6%
ValueCountFrequency (%)
3891
< 0.1%
3521
< 0.1%
3481
< 0.1%
3431
< 0.1%
3401
< 0.1%
3371
< 0.1%
3331
< 0.1%
3281
< 0.1%
3221
< 0.1%
3191
< 0.1%

Interactions

2021-03-31T20:51:14.292744image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:14.693316image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:14.861639image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:15.021774image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:15.195118image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:15.361562image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:15.720258image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:16.004614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:16.695865image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:16.873119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:17.037403image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:17.202955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:17.381270image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:17.548729image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:17.721831image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:17.898451image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.076643image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.227076image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.382354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.522867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.673706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.815363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:18.961855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:19.113669image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:19.265918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:19.418855image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:19.579424image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:19.719833image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:19.875629image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.019380image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.175686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.328592image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.485112image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.657996image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.834832image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:20.994138image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:21.153894image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:21.313525image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:21.481115image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:21.656534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:21.828706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:21.984706image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:22.144681image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:22.285047image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:22.625299image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:22.785704image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:22.936676image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:23.090952image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:23.247572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:23.411297image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:23.580570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:23.732598image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:23.885106image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:24.051387image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:24.205689image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:24.369185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:24.545884image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:24.712701image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:24.884646image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:25.038586image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:25.193209image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:25.359555image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:25.517148image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:25.680980image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:25.851578image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:26.023487image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:26.200212image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:26.357957image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:26.516943image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:26.688891image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:26.848470image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-03-31T20:51:27.016037image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-03-31T20:51:32.996589image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-31T20:51:33.221090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-31T20:51:33.443719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-31T20:51:33.675234image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-31T20:51:33.878557image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-31T20:51:27.363197image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-31T20:51:27.718311image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

gameIdchampiontimestamprolelaneaccountIdparticipantIdwinkillsdeathsassistsvisionScoregoldEarnedtotalMinionsKilled
02165199251401611135706412DUO_SUPPORTBOTTOM2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs9True1130871026427
12165300614401611138832262DUO_SUPPORTBOTTOM2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs2False061072660410
221657825231171611184446397DUO_SUPPORTNONE2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs8False1362349976
32165726753401611185960317DUO_SUPPORTNONE2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs8False0354044627
42165789161401611187745416DUO_SUPPORTNONE2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs8True12112763924
521658440511171611189912698DUO_SUPPORTBOTTOM2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs9True4522801105229
62165920920401611193238832DUO_SUPPORTBOTTOM2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs9True44171001056114
721658866321171611195601251DUO_SUPPORTBOTTOM2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs8True022174823920
82166011748401611198379073DUO_SUPPORTBOTTOM2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs7True3318911034720
92166015569401611201202132DUO_SUPPORTTOP2dzIXJUqmdWNfeAOMvRcJ5eHVFAmcUeqI693veYS8W1zrHs8False06733477810

Last rows

gameIdchampiontimestamprolelaneaccountIdparticipantIdwinkillsdeathsassistsvisionScoregoldEarnedtotalMinionsKilled
4066219405424081613914069163SOLOMIDQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U10False2811139494144
4067219406600081613916181684SOLOTOPQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U7True6561811521154
4068219411128881613920740685SOLOMIDQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U2False450148683151
4069219411392381613922762134DUO_CARRYMIDQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U3False473139270164
4070219411898081613926433139SOLOMIDQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U9True83101912778209
4071219420308981613929072940SOLOMIDQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U9True16542214356162
407221952604411611614033459187DUO_SUPPORTMIDQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U9True571501320954
407321952340264291614035034585DUONONEQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U3True621101035366
407421952662804981614036325671DUOTOPQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U1False221024019492130
4075219608902981614126341524SOLOTOPQv8KNeAUdg3eMe_MyO1h8W_oPg_2BrcdhIHKaf4G9iQWO0U2True3772311764204